ANCOVA in Vocabulary
(Vocabulary)
Geiser C. Challco geiser@alumni.usp.br
NOTE:
- Teste ANCOVA para determinar se houve diferenças significativas no
Vocabulary (medido usando pre- e pos-testes).
- ANCOVA test to determine whether there were significant differences
in Vocabulary (measured using pre- and post-tests).
Setting Initial Variables
dv = "vocab"
dv.pos = "vocab.pos"
dv.pre = "vocab.pre"
fatores2 <- c("Sexo","Zona","Cor.Raca","Serie","vocab.quintile")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2
fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1
lfatores <- c(lfatores1)
color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#fd7f6f")
color[["Sexo"]] = c("#FF007F","#4D4DFF")
color[["Zona"]] = c("#AA00FF","#00CCCC")
color[["Cor.Raca"]] = c(
"Parda"="#b97100","Indígena"="#9F262F",
"Branca"="#87c498", "Preta"="#848283","Amarela"="#D6B91C"
)
level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["Sexo"]] = c("F","M")
level[["Zona"]] = c("Rural","Urbana")
level[["Cor.Raca"]] = c("Parda","Indígena","Branca", "Preta","Amarela")
level[["Serie"]] = c("6 ano","7 ano","8 ano","9 ano")
# ..
ymin <- 0
ymax <- 0
ymin.ci <- 0
ymax.ci <- 0
color[["grupo:Sexo"]] = c(
"Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
"Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
"Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
"Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:Zona"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:Cor.Raca"]] = c(
"Controle:Parda"="#e3c699", "Experimental:Parda"="#b97100",
"Controle:Indígena"="#e2bdc0", "Experimental:Indígena"="#9F262F",
"Controle:Branca"="#c0e8cb", "Experimental:Branca"="#87c498",
"Controle:Preta"="#dad9d9", "Experimental:Preta"="#848283",
"Controle:Amarela"="#eee3a4", "Experimental:Amarela"="#D6B91C",
"Controle.Parda"="#e3c699", "Experimental.Parda"="#b97100",
"Controle.Indígena"="#e2bdc0", "Experimental.Indígena"="#9F262F",
"Controle.Branca"="#c0e8cb", "Experimental.Branca"="#87c498",
"Controle.Preta"="#dad9d9", "Experimental.Preta"="#848283",
"Controle.Amarela"="#eee3a4", "Experimental.Amarela"="#D6B91C"
)
for (coln in c("vocab","vocab.teach","vocab.non.teach","score.tde",
"TFL.lidas.per.min","TFL.corretas.per.min","TFL.erradas.per.min","TFL.omitidas.per.min",
"leitura.compreensao")) {
color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
color[[paste0("grupo:",coln,".quintile")]] = c(
"Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
"Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
"Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
"Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
"Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
"Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
"Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
"Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
"Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
"Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}
gdat <- read_excel("../data/data.xlsx", sheet = "sumary")
gdat <- gdat[which(is.na(gdat$Necessidade.Deficiencia) & !is.na(gdat$Stari.Grupo)),]
dat <- gdat
dat$grupo <- factor(dat[["Stari.Grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]
dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])
for (f in c("grupo", names(lfatores))) {
if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0)
color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}
ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()
Descriptive Statistics
of Initial Data
df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
|
vocab.pre |
98 |
23.980 |
23.0 |
7 |
45 |
7.824 |
0.790 |
1.569 |
10.00 |
YES |
0.389 |
-0.356 |
| Experimental |
|
|
|
|
|
vocab.pre |
48 |
23.208 |
24.0 |
7 |
39 |
7.092 |
1.024 |
2.059 |
9.50 |
YES |
-0.315 |
-0.462 |
|
|
|
|
|
|
vocab.pre |
146 |
23.726 |
24.0 |
7 |
45 |
7.575 |
0.627 |
1.239 |
10.00 |
YES |
0.216 |
-0.265 |
| Controle |
|
|
|
|
|
vocab.pos |
98 |
24.633 |
23.0 |
11 |
47 |
8.458 |
0.854 |
1.696 |
11.50 |
NO |
0.597 |
-0.409 |
| Experimental |
|
|
|
|
|
vocab.pos |
48 |
24.333 |
24.5 |
8 |
40 |
7.603 |
1.097 |
2.208 |
10.25 |
YES |
-0.015 |
-0.642 |
|
|
|
|
|
|
vocab.pos |
146 |
24.534 |
23.0 |
8 |
47 |
8.161 |
0.675 |
1.335 |
10.75 |
YES |
0.447 |
-0.386 |
| Controle |
F |
|
|
|
|
vocab.pre |
43 |
24.535 |
25.0 |
12 |
36 |
6.874 |
1.048 |
2.116 |
9.00 |
YES |
-0.003 |
-1.003 |
| Controle |
M |
|
|
|
|
vocab.pre |
55 |
23.545 |
22.0 |
7 |
45 |
8.531 |
1.150 |
2.306 |
12.00 |
NO |
0.588 |
-0.233 |
| Experimental |
F |
|
|
|
|
vocab.pre |
16 |
24.000 |
25.5 |
11 |
33 |
6.976 |
1.744 |
3.717 |
7.75 |
NO |
-0.631 |
-0.789 |
| Experimental |
M |
|
|
|
|
vocab.pre |
32 |
22.812 |
23.5 |
7 |
39 |
7.226 |
1.277 |
2.605 |
10.25 |
YES |
-0.148 |
-0.386 |
| Controle |
F |
|
|
|
|
vocab.pos |
43 |
24.907 |
23.0 |
14 |
40 |
6.938 |
1.058 |
2.135 |
7.50 |
NO |
0.698 |
-0.419 |
| Controle |
M |
|
|
|
|
vocab.pos |
55 |
24.418 |
23.0 |
11 |
47 |
9.537 |
1.286 |
2.578 |
13.50 |
NO |
0.563 |
-0.687 |
| Experimental |
F |
|
|
|
|
vocab.pos |
16 |
24.062 |
23.0 |
17 |
40 |
5.767 |
1.442 |
3.073 |
9.00 |
NO |
1.055 |
1.067 |
| Experimental |
M |
|
|
|
|
vocab.pos |
32 |
24.469 |
25.5 |
8 |
39 |
8.455 |
1.495 |
3.048 |
13.00 |
YES |
-0.210 |
-1.095 |
| Controle |
|
Rural |
|
|
|
vocab.pre |
56 |
23.607 |
23.0 |
12 |
44 |
7.065 |
0.944 |
1.892 |
9.50 |
YES |
0.382 |
-0.340 |
| Controle |
|
Urbana |
|
|
|
vocab.pre |
11 |
22.455 |
20.0 |
7 |
45 |
10.539 |
3.178 |
7.080 |
9.50 |
NO |
0.669 |
-0.409 |
| Controle |
|
|
|
|
|
vocab.pre |
31 |
25.194 |
24.0 |
12 |
42 |
8.171 |
1.468 |
2.997 |
12.50 |
YES |
0.173 |
-1.104 |
| Experimental |
|
Rural |
|
|
|
vocab.pre |
34 |
22.618 |
24.0 |
7 |
39 |
7.274 |
1.247 |
2.538 |
8.50 |
YES |
-0.208 |
-0.361 |
| Experimental |
|
Urbana |
|
|
|
vocab.pre |
5 |
26.200 |
26.0 |
15 |
33 |
7.463 |
3.338 |
9.267 |
9.00 |
YES |
-0.379 |
-1.708 |
| Experimental |
|
|
|
|
|
vocab.pre |
9 |
23.778 |
24.0 |
13 |
30 |
6.476 |
2.159 |
4.978 |
6.00 |
NO |
-0.609 |
-1.314 |
| Controle |
|
Rural |
|
|
|
vocab.pos |
56 |
24.821 |
23.0 |
11 |
47 |
8.651 |
1.156 |
2.317 |
11.25 |
YES |
0.483 |
-0.337 |
| Controle |
|
Urbana |
|
|
|
vocab.pos |
11 |
22.091 |
20.0 |
12 |
41 |
8.166 |
2.462 |
5.486 |
7.50 |
NO |
0.985 |
0.027 |
| Controle |
|
|
|
|
|
vocab.pos |
31 |
25.194 |
23.0 |
14 |
41 |
8.312 |
1.493 |
3.049 |
10.50 |
NO |
0.641 |
-0.930 |
| Experimental |
|
Rural |
|
|
|
vocab.pos |
34 |
24.059 |
24.0 |
8 |
39 |
7.847 |
1.346 |
2.738 |
10.75 |
YES |
-0.142 |
-0.792 |
| Experimental |
|
Urbana |
|
|
|
vocab.pos |
5 |
23.400 |
23.0 |
16 |
34 |
7.127 |
3.187 |
8.850 |
8.00 |
YES |
0.357 |
-1.720 |
| Experimental |
|
|
|
|
|
vocab.pos |
9 |
25.889 |
26.0 |
15 |
40 |
7.524 |
2.508 |
5.783 |
8.00 |
YES |
0.380 |
-0.948 |
| Controle |
|
|
Parda |
|
|
vocab.pre |
45 |
23.156 |
23.0 |
12 |
44 |
7.517 |
1.120 |
2.258 |
10.00 |
YES |
0.488 |
-0.335 |
| Controle |
|
|
Indígena |
|
|
vocab.pre |
3 |
27.000 |
26.0 |
24 |
31 |
3.606 |
2.082 |
8.957 |
3.50 |
few data |
0.000 |
0.000 |
| Controle |
|
|
Branca |
|
|
vocab.pre |
11 |
23.909 |
27.0 |
14 |
31 |
6.363 |
1.919 |
4.275 |
11.00 |
YES |
-0.423 |
-1.709 |
| Controle |
|
|
Preta |
|
|
vocab.pre |
1 |
19.000 |
19.0 |
19 |
19 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
|
vocab.pre |
38 |
24.868 |
23.0 |
7 |
45 |
8.866 |
1.438 |
2.914 |
14.00 |
YES |
0.315 |
-0.707 |
| Experimental |
|
|
Parda |
|
|
vocab.pre |
18 |
21.389 |
22.0 |
7 |
39 |
8.925 |
2.104 |
4.439 |
12.75 |
YES |
0.068 |
-1.053 |
| Experimental |
|
|
Indígena |
|
|
vocab.pre |
6 |
24.833 |
23.5 |
18 |
33 |
5.345 |
2.182 |
5.609 |
5.50 |
YES |
0.293 |
-1.569 |
| Experimental |
|
|
Branca |
|
|
vocab.pre |
5 |
21.200 |
23.0 |
15 |
28 |
5.541 |
2.478 |
6.880 |
8.00 |
YES |
-0.043 |
-2.062 |
| Experimental |
|
|
|
|
|
vocab.pre |
19 |
24.947 |
25.0 |
13 |
33 |
5.730 |
1.315 |
2.762 |
6.00 |
NO |
-0.594 |
-0.555 |
| Controle |
|
|
Parda |
|
|
vocab.pos |
45 |
23.622 |
23.0 |
11 |
45 |
7.904 |
1.178 |
2.375 |
7.00 |
NO |
0.612 |
-0.003 |
| Controle |
|
|
Indígena |
|
|
vocab.pos |
3 |
28.000 |
27.0 |
25 |
32 |
3.606 |
2.082 |
8.957 |
3.50 |
few data |
0.000 |
0.000 |
| Controle |
|
|
Branca |
|
|
vocab.pos |
11 |
25.727 |
27.0 |
14 |
40 |
8.296 |
2.501 |
5.573 |
13.00 |
YES |
0.140 |
-1.465 |
| Controle |
|
|
Preta |
|
|
vocab.pos |
1 |
23.000 |
23.0 |
23 |
23 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
|
vocab.pos |
38 |
25.289 |
23.0 |
12 |
47 |
9.535 |
1.547 |
3.134 |
12.75 |
NO |
0.607 |
-0.904 |
| Experimental |
|
|
Parda |
|
|
vocab.pos |
18 |
22.111 |
21.0 |
8 |
39 |
8.217 |
1.937 |
4.086 |
11.50 |
YES |
0.331 |
-0.790 |
| Experimental |
|
|
Indígena |
|
|
vocab.pos |
6 |
21.667 |
24.0 |
10 |
28 |
6.653 |
2.716 |
6.982 |
6.50 |
NO |
-0.714 |
-1.231 |
| Experimental |
|
|
Branca |
|
|
vocab.pos |
5 |
28.000 |
31.0 |
16 |
35 |
7.450 |
3.332 |
9.250 |
6.00 |
NO |
-0.630 |
-1.475 |
| Experimental |
|
|
|
|
|
vocab.pos |
19 |
26.316 |
26.0 |
14 |
40 |
6.880 |
1.578 |
3.316 |
7.50 |
YES |
0.123 |
-0.695 |
| Controle |
|
|
|
6 ano |
|
vocab.pre |
26 |
19.423 |
18.5 |
12 |
29 |
5.551 |
1.089 |
2.242 |
8.50 |
YES |
0.275 |
-1.373 |
| Controle |
|
|
|
7 ano |
|
vocab.pre |
28 |
23.286 |
23.0 |
12 |
35 |
5.974 |
1.129 |
2.317 |
9.00 |
YES |
0.142 |
-0.813 |
| Controle |
|
|
|
8 ano |
|
vocab.pre |
17 |
23.882 |
19.0 |
7 |
45 |
10.688 |
2.592 |
5.495 |
16.00 |
NO |
0.658 |
-0.739 |
| Controle |
|
|
|
9 ano |
|
vocab.pre |
27 |
29.148 |
29.0 |
13 |
42 |
6.509 |
1.253 |
2.575 |
9.50 |
YES |
-0.370 |
-0.275 |
| Experimental |
|
|
|
6 ano |
|
vocab.pre |
13 |
22.385 |
24.0 |
9 |
30 |
7.066 |
1.960 |
4.270 |
10.00 |
NO |
-0.583 |
-1.102 |
| Experimental |
|
|
|
7 ano |
|
vocab.pre |
13 |
23.692 |
23.0 |
13 |
39 |
7.674 |
2.129 |
4.638 |
8.00 |
YES |
0.455 |
-0.930 |
| Experimental |
|
|
|
8 ano |
|
vocab.pre |
14 |
22.357 |
23.5 |
7 |
33 |
8.409 |
2.247 |
4.855 |
14.25 |
YES |
-0.363 |
-1.307 |
| Experimental |
|
|
|
9 ano |
|
vocab.pre |
8 |
25.250 |
25.0 |
19 |
29 |
3.454 |
1.221 |
2.887 |
4.50 |
YES |
-0.403 |
-1.235 |
| Controle |
|
|
|
6 ano |
|
vocab.pos |
26 |
18.692 |
18.5 |
11 |
27 |
4.994 |
0.979 |
2.017 |
8.25 |
YES |
0.037 |
-1.282 |
| Controle |
|
|
|
7 ano |
|
vocab.pos |
28 |
26.464 |
24.0 |
12 |
47 |
8.194 |
1.549 |
3.177 |
8.50 |
NO |
0.644 |
-0.211 |
| Controle |
|
|
|
8 ano |
|
vocab.pos |
17 |
24.000 |
21.0 |
14 |
45 |
9.605 |
2.329 |
4.938 |
11.00 |
NO |
0.940 |
-0.562 |
| Controle |
|
|
|
9 ano |
|
vocab.pos |
27 |
28.852 |
29.0 |
14 |
41 |
7.665 |
1.475 |
3.032 |
10.00 |
YES |
-0.076 |
-1.152 |
| Experimental |
|
|
|
6 ano |
|
vocab.pos |
13 |
23.538 |
25.0 |
14 |
35 |
6.280 |
1.742 |
3.795 |
9.00 |
YES |
-0.102 |
-1.103 |
| Experimental |
|
|
|
7 ano |
|
vocab.pos |
13 |
23.077 |
22.0 |
10 |
39 |
8.401 |
2.330 |
5.077 |
10.00 |
YES |
0.127 |
-1.001 |
| Experimental |
|
|
|
8 ano |
|
vocab.pos |
14 |
24.857 |
25.0 |
8 |
37 |
8.690 |
2.323 |
5.017 |
13.25 |
YES |
-0.248 |
-1.225 |
| Experimental |
|
|
|
9 ano |
|
vocab.pos |
8 |
26.750 |
25.5 |
18 |
40 |
6.923 |
2.448 |
5.788 |
7.25 |
NO |
0.589 |
-0.890 |
| Controle |
|
|
|
|
1st quintile |
vocab.pre |
17 |
13.529 |
14.0 |
7 |
16 |
2.267 |
0.550 |
1.166 |
3.00 |
NO |
-1.142 |
1.450 |
| Controle |
|
|
|
|
2nd quintile |
vocab.pre |
28 |
19.286 |
19.0 |
17 |
22 |
1.462 |
0.276 |
0.567 |
1.00 |
YES |
0.273 |
-0.622 |
| Controle |
|
|
|
|
3rd quintile |
vocab.pre |
31 |
26.097 |
27.0 |
23 |
29 |
2.166 |
0.389 |
0.794 |
4.00 |
YES |
-0.216 |
-1.517 |
| Controle |
|
|
|
|
4th quintile |
vocab.pre |
9 |
31.667 |
31.0 |
30 |
34 |
1.414 |
0.471 |
1.087 |
2.00 |
YES |
0.288 |
-1.556 |
| Controle |
|
|
|
|
5th quintile |
vocab.pre |
13 |
37.385 |
36.0 |
35 |
45 |
3.664 |
1.016 |
2.214 |
1.00 |
NO |
1.171 |
-0.476 |
| Experimental |
|
|
|
|
1st quintile |
vocab.pre |
9 |
12.111 |
13.0 |
7 |
16 |
2.892 |
0.964 |
2.223 |
3.00 |
YES |
-0.346 |
-1.265 |
| Experimental |
|
|
|
|
2nd quintile |
vocab.pre |
8 |
19.125 |
19.0 |
17 |
22 |
1.808 |
0.639 |
1.511 |
2.50 |
YES |
0.220 |
-1.553 |
| Experimental |
|
|
|
|
3rd quintile |
vocab.pre |
21 |
25.286 |
25.0 |
23 |
29 |
1.978 |
0.432 |
0.901 |
2.00 |
NO |
0.544 |
-1.059 |
| Experimental |
|
|
|
|
4th quintile |
vocab.pre |
9 |
31.333 |
30.0 |
30 |
33 |
1.581 |
0.527 |
1.215 |
3.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
5th quintile |
vocab.pre |
1 |
39.000 |
39.0 |
39 |
39 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
1st quintile |
vocab.pos |
17 |
17.294 |
18.0 |
11 |
23 |
3.771 |
0.915 |
1.939 |
5.00 |
YES |
-0.367 |
-1.195 |
| Controle |
|
|
|
|
2nd quintile |
vocab.pos |
28 |
21.393 |
21.5 |
12 |
47 |
7.233 |
1.367 |
2.805 |
6.75 |
NO |
1.512 |
3.304 |
| Controle |
|
|
|
|
3rd quintile |
vocab.pos |
31 |
25.613 |
25.0 |
14 |
40 |
6.009 |
1.079 |
2.204 |
4.50 |
YES |
0.435 |
0.073 |
| Controle |
|
|
|
|
4th quintile |
vocab.pos |
9 |
32.556 |
32.0 |
16 |
40 |
7.248 |
2.416 |
5.571 |
7.00 |
NO |
-1.053 |
0.290 |
| Controle |
|
|
|
|
5th quintile |
vocab.pos |
13 |
33.385 |
38.0 |
17 |
45 |
9.106 |
2.526 |
5.503 |
13.00 |
NO |
-0.582 |
-1.250 |
| Experimental |
|
|
|
|
1st quintile |
vocab.pos |
9 |
17.333 |
16.0 |
8 |
31 |
6.837 |
2.279 |
5.256 |
4.00 |
NO |
0.611 |
-0.655 |
| Experimental |
|
|
|
|
2nd quintile |
vocab.pos |
8 |
19.000 |
19.5 |
10 |
30 |
6.188 |
2.188 |
5.173 |
6.75 |
YES |
0.260 |
-1.092 |
| Experimental |
|
|
|
|
3rd quintile |
vocab.pos |
21 |
27.238 |
26.0 |
18 |
40 |
6.057 |
1.322 |
2.757 |
9.00 |
YES |
0.433 |
-0.869 |
| Experimental |
|
|
|
|
4th quintile |
vocab.pos |
9 |
27.667 |
28.0 |
19 |
34 |
4.743 |
1.581 |
3.646 |
5.00 |
YES |
-0.365 |
-1.109 |
| Experimental |
|
|
|
|
5th quintile |
vocab.pos |
1 |
39.000 |
39.0 |
39 |
39 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
ANCOVA and Pairwise
for one factor: grupo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "vocab.pos", "grupo")
pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab"]] <- c(pdat[["vocab.pre"]], pdat[["vocab.pos"]])
aov = anova_test(pdat, vocab.pos ~ vocab.pre + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, vocab.pos ~ grupo, covariate = vocab.pre,
p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "vocab.pos", "grupo", covar = "vocab.pre")
ds <- merge(ds[ds$variable != "vocab.pre",],
ds[ds$variable == "vocab.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".vocab.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.vocab.pre","se.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- ds
Computing
ANCOVA and PairWise After removing non-normal data (OK)
wdat = pdat
res = residuals(lm(vocab.pos ~ vocab.pre + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab"]] <- c(wdat[["vocab.pre"]], wdat[["vocab.pos"]])
ldat[["grupo"]] = wdat
(non.normal)
## [1] "P3569"
aov = anova_test(wdat, vocab.pos ~ vocab.pre + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
by="Effect", suffixes = c("","'"))
(df = get_anova_table(aov))
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 vocab.pre 1 142 126.192 2.39e-21 * 0.471
## 2 grupo 1 142 0.258 6.12e-01 0.002
| vocab.pre |
1 |
142 |
126.192 |
0.000 |
* |
0.471 |
| grupo |
1 |
142 |
0.258 |
0.612 |
|
0.002 |
pwc <- emmeans_test(wdat, vocab.pos ~ grupo, covariate = vocab.pre,
p.adjust.method = "bonferroni")
| vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
142 |
-0.508 |
0.612 |
0.612 |
ns |
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
by=c("grupo","term",".y.","group1","group2"),
suffixes = c("","'"))
| Controle |
time |
vocab |
pre |
pos |
286 |
-0.331 |
0.741 |
0.741 |
ns |
| Experimental |
time |
vocab |
pre |
pos |
286 |
-0.706 |
0.481 |
0.481 |
ns |
ds <- get.descriptives(wdat, "vocab.pos", "grupo", covar = "vocab.pre")
ds <- merge(ds[ds$variable != "vocab.pre",],
ds[ds$variable == "vocab.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".vocab.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.vocab.pre","se.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
| Controle |
97 |
24.031 |
0.797 |
24.402 |
0.831 |
24.206 |
0.593 |
23.033 |
25.379 |
| Experimental |
48 |
23.208 |
1.024 |
24.333 |
1.097 |
24.730 |
0.844 |
23.062 |
26.398 |
Plots for ancova
plots <- oneWayAncovaPlots(
wdat, "vocab.pos", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
font.label.size=10, step.increase=0.05, p.label="p.adj",
subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
plots[["grupo"]] +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
wdat, "vocab.pos", "grupo", aov, pwc, covar = "vocab.pre",
theme = "classic", color = color[["grupo"]],
subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Vocabulary") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
plots <- oneWayAncovaBoxPlots(
wdat.long, "vocab", "grupo", aov, pwc.long,
pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
color = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking normality and
homogeneity
res <- augment(lm(vocab.pos ~ vocab.pre + grupo, data = wdat))
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.995 0.914
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 1 143 0.0402 0.841
ANCOVA and
Pairwise for two factors grupo:Sexo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Sexo"]]),],
"vocab.pos", c("grupo","Sexo"))
pdat = pdat[pdat[["Sexo"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Sexo"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Sexo"]] = factor(
pdat[["Sexo"]],
level[["Sexo"]][level[["Sexo"]] %in% unique(pdat[["Sexo"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Sexo")], pdat[,c("id","grupo","Sexo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab"]] <- c(pdat[["vocab.pre"]], pdat[["vocab.pos"]])
if (length(unique(pdat[["Sexo"]])) >= 2) {
aov = anova_test(pdat, vocab.pos ~ vocab.pre + grupo*Sexo)
laov[["grupo:Sexo"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwcs <- list()
pwcs[["Sexo"]] <- emmeans_test(
group_by(pdat, grupo), vocab.pos ~ Sexo,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Sexo), vocab.pos ~ grupo,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Sexo")),
vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Sexo"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.pos", c("grupo","Sexo"), covar = "vocab.pre")
ds <- merge(ds[ds$variable != "vocab.pre",],
ds[ds$variable == "vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Sexo"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Sexo","n","mean.vocab.pre","se.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Sexo"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Sexo"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.pos ~ vocab.pre + grupo*Sexo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Sexo")], wdat[,c("id","grupo","Sexo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab"]] <- c(wdat[["vocab.pre"]], wdat[["vocab.pos"]])
ldat[["grupo:Sexo"]] = wdat
(non.normal)
}
## [1] "P3569"
if (length(unique(pdat[["Sexo"]])) >= 2) {
aov = anova_test(wdat, vocab.pos ~ vocab.pre + grupo*Sexo)
laov[["grupo:Sexo"]] <- merge(get_anova_table(aov), laov[["grupo:Sexo"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| vocab.pre |
1 |
140 |
124.810 |
0.000 |
* |
0.471 |
| grupo |
1 |
140 |
0.231 |
0.631 |
|
0.002 |
| Sexo |
1 |
140 |
0.046 |
0.831 |
|
0.000 |
| grupo:Sexo |
1 |
140 |
0.493 |
0.484 |
|
0.004 |
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwcs <- list()
pwcs[["Sexo"]] <- emmeans_test(
group_by(wdat, grupo), vocab.pos ~ Sexo,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Sexo), vocab.pos ~ grupo,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
|
F |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
140 |
0.266 |
0.790 |
0.790 |
ns |
|
M |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
140 |
-0.808 |
0.420 |
0.420 |
ns |
| Controle |
|
vocab.pre*Sexo |
vocab.pos |
F |
M |
140 |
0.211 |
0.834 |
0.834 |
ns |
| Experimental |
|
vocab.pre*Sexo |
vocab.pos |
F |
M |
140 |
-0.703 |
0.483 |
0.483 |
ns |
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Sexo")),
vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Sexo"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Sexo"]],
by=c("grupo","Sexo","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
F |
time |
vocab |
pre |
pos |
282 |
-0.220 |
0.826 |
0.826 |
ns |
| Controle |
M |
time |
vocab |
pre |
pos |
282 |
-0.245 |
0.806 |
0.806 |
ns |
| Experimental |
F |
time |
vocab |
pre |
pos |
282 |
-0.023 |
0.982 |
0.982 |
ns |
| Experimental |
M |
time |
vocab |
pre |
pos |
282 |
-0.844 |
0.399 |
0.399 |
ns |
if (length(unique(pdat[["Sexo"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.pos", c("grupo","Sexo"), covar = "vocab.pre")
ds <- merge(ds[ds$variable != "vocab.pre",],
ds[ds$variable == "vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Sexo"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Sexo","n","mean.vocab.pre","se.vocab.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Sexo"]] <- merge(ds, lemms[["grupo:Sexo"]],
by=c("grupo","Sexo"), suffixes = c("","'"))
}
| Controle |
F |
43 |
24.535 |
1.048 |
24.907 |
1.058 |
24.346 |
0.897 |
22.573 |
26.119 |
| Controle |
M |
54 |
23.630 |
1.169 |
24.000 |
1.239 |
24.093 |
0.799 |
22.513 |
25.673 |
| Experimental |
F |
16 |
24.000 |
1.744 |
24.062 |
1.442 |
23.888 |
1.468 |
20.986 |
26.790 |
| Experimental |
M |
32 |
22.812 |
1.277 |
24.469 |
1.495 |
25.152 |
1.040 |
23.097 |
27.208 |
Plots for ancova
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Sexo", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Sexo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggPlotAoC2(pwcs, "Sexo", "grupo", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.pos", c("grupo","Sexo"), aov, pwcs, covar = "vocab.pre",
theme = "classic", color = color[["grupo:Sexo"]],
subtitle = which(aov$Effect == "grupo:Sexo"))
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
plots[["grupo:Sexo"]] + ggplot2::ylab("Vocabulary") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Sexo"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab", c("grupo","Sexo"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Sexo"]])) >= 2)
plots[["grupo:Sexo"]] + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
facet.by = c("grupo","Sexo"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
color = "grupo", facet.by = "Sexo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
color = "Sexo", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Sexo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
ggplot2::scale_color_manual(values = color[["Sexo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Sexo"]])) >= 2)
res <- augment(lm(vocab.pos ~ vocab.pre + grupo*Sexo, data = wdat))
if (length(unique(pdat[["Sexo"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.995 0.917
if (length(unique(pdat[["Sexo"]])) >= 2)
levene_test(res, .resid ~ grupo*Sexo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 141 0.636 0.593
ANCOVA and
Pairwise for two factors grupo:Zona
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Zona"]]),],
"vocab.pos", c("grupo","Zona"))
pdat = pdat[pdat[["Zona"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Zona"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Zona"]] = factor(
pdat[["Zona"]],
level[["Zona"]][level[["Zona"]] %in% unique(pdat[["Zona"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Zona")], pdat[,c("id","grupo","Zona")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab"]] <- c(pdat[["vocab.pre"]], pdat[["vocab.pos"]])
if (length(unique(pdat[["Zona"]])) >= 2) {
aov = anova_test(pdat, vocab.pos ~ vocab.pre + grupo*Zona)
laov[["grupo:Zona"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
pwcs <- list()
pwcs[["Zona"]] <- emmeans_test(
group_by(pdat, grupo), vocab.pos ~ Zona,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Zona), vocab.pos ~ grupo,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
if (length(unique(pdat[["Zona"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Zona")),
vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Zona"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.pos", c("grupo","Zona"), covar = "vocab.pre")
ds <- merge(ds[ds$variable != "vocab.pre",],
ds[ds$variable == "vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Zona"), all.x = T, suffixes = c("", ".vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Zona"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Zona","n","mean.vocab.pre","se.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Zona"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Zona"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.pos ~ vocab.pre + grupo*Zona, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Zona")], wdat[,c("id","grupo","Zona")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab"]] <- c(wdat[["vocab.pre"]], wdat[["vocab.pos"]])
ldat[["grupo:Zona"]] = wdat
(non.normal)
}
## [1] "P3569"
if (length(unique(pdat[["Zona"]])) >= 2) {
aov = anova_test(wdat, vocab.pos ~ vocab.pre + grupo*Zona)
laov[["grupo:Zona"]] <- merge(get_anova_table(aov), laov[["grupo:Zona"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| vocab.pre |
1 |
100 |
140.558 |
0.000 |
* |
0.584 |
| grupo |
1 |
100 |
0.034 |
0.854 |
|
0.000 |
| Zona |
1 |
100 |
2.086 |
0.152 |
|
0.020 |
| grupo:Zona |
1 |
100 |
0.538 |
0.465 |
|
0.005 |
if (length(unique(pdat[["Zona"]])) >= 2) {
pwcs <- list()
pwcs[["Zona"]] <- emmeans_test(
group_by(wdat, grupo), vocab.pos ~ Zona,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Zona), vocab.pos ~ grupo,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
|
Rural |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
100 |
-0.448 |
0.655 |
0.655 |
ns |
|
Urbana |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
100 |
0.612 |
0.542 |
0.542 |
ns |
| Controle |
|
vocab.pre*Zona |
vocab.pos |
Rural |
Urbana |
100 |
0.770 |
0.443 |
0.443 |
ns |
| Experimental |
|
vocab.pre*Zona |
vocab.pos |
Rural |
Urbana |
100 |
1.421 |
0.158 |
0.158 |
ns |
if (length(unique(pdat[["Zona"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Zona")),
vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Zona"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Zona"]],
by=c("grupo","Zona","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
vocab |
pre |
pos |
202 |
-0.488 |
0.626 |
0.626 |
ns |
| Controle |
Urbana |
time |
vocab |
pre |
pos |
202 |
0.109 |
0.913 |
0.913 |
ns |
| Experimental |
Rural |
time |
vocab |
pre |
pos |
202 |
-0.761 |
0.448 |
0.448 |
ns |
| Experimental |
Urbana |
time |
vocab |
pre |
pos |
202 |
0.567 |
0.571 |
0.571 |
ns |
if (length(unique(pdat[["Zona"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.pos", c("grupo","Zona"), covar = "vocab.pre")
ds <- merge(ds[ds$variable != "vocab.pre",],
ds[ds$variable == "vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Zona"), all.x = T, suffixes = c("", ".vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Zona"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Zona","n","mean.vocab.pre","se.vocab.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Zona"]] <- merge(ds, lemms[["grupo:Zona"]],
by=c("grupo","Zona"), suffixes = c("","'"))
}
| Controle |
Rural |
55 |
23.691 |
0.958 |
24.418 |
1.103 |
24.129 |
0.702 |
22.735 |
25.522 |
| Controle |
Urbana |
11 |
22.455 |
3.178 |
22.091 |
2.462 |
22.803 |
1.571 |
19.687 |
25.919 |
| Experimental |
Rural |
34 |
22.618 |
1.247 |
24.059 |
1.346 |
24.639 |
0.894 |
22.865 |
26.412 |
| Experimental |
Urbana |
5 |
26.200 |
3.338 |
23.400 |
3.187 |
21.078 |
2.336 |
16.443 |
25.712 |
Plots for ancova
if (length(unique(pdat[["Zona"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Zona", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Zona"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
ggPlotAoC2(pwcs, "Zona", "grupo", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.pos", c("grupo","Zona"), aov, pwcs, covar = "vocab.pre",
theme = "classic", color = color[["grupo:Zona"]],
subtitle = which(aov$Effect == "grupo:Zona"))
}
if (length(unique(pdat[["Zona"]])) >= 2) {
plots[["grupo:Zona"]] + ggplot2::ylab("Vocabulary") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Zona"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab", c("grupo","Zona"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Zona"]])) >= 2)
plots[["grupo:Zona"]] + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
facet.by = c("grupo","Zona"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
color = "grupo", facet.by = "Zona", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
color = "Zona", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Zona)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
ggplot2::scale_color_manual(values = color[["Zona"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Zona"]])) >= 2)
res <- augment(lm(vocab.pos ~ vocab.pre + grupo*Zona, data = wdat))
if (length(unique(pdat[["Zona"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.992 0.826
if (length(unique(pdat[["Zona"]])) >= 2)
levene_test(res, .resid ~ grupo*Zona)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 101 0.234 0.873
ANCOVA and
Pairwise for two factors grupo:Cor.Raca
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Cor.Raca"]]),],
"vocab.pos", c("grupo","Cor.Raca"))
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
pdat = pdat[pdat[["Cor.Raca"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Cor.Raca"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Cor.Raca"]] = factor(
pdat[["Cor.Raca"]],
level[["Cor.Raca"]][level[["Cor.Raca"]] %in% unique(pdat[["Cor.Raca"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Cor.Raca")], pdat[,c("id","grupo","Cor.Raca")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab"]] <- c(pdat[["vocab.pre"]], pdat[["vocab.pos"]])
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
aov = anova_test(pdat, vocab.pos ~ vocab.pre + grupo*Cor.Raca)
laov[["grupo:Cor.Raca"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwcs <- list()
pwcs[["Cor.Raca"]] <- emmeans_test(
group_by(pdat, grupo), vocab.pos ~ Cor.Raca,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Cor.Raca), vocab.pos ~ grupo,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Cor.Raca")),
vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Cor.Raca"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.pos", c("grupo","Cor.Raca"), covar = "vocab.pre")
ds <- merge(ds[ds$variable != "vocab.pre",],
ds[ds$variable == "vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Cor.Raca","n","mean.vocab.pre","se.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Cor.Raca"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.pos ~ vocab.pre + grupo*Cor.Raca, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Cor.Raca")], wdat[,c("id","grupo","Cor.Raca")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab"]] <- c(wdat[["vocab.pre"]], wdat[["vocab.pos"]])
ldat[["grupo:Cor.Raca"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
aov = anova_test(wdat, vocab.pos ~ vocab.pre + grupo*Cor.Raca)
laov[["grupo:Cor.Raca"]] <- merge(get_anova_table(aov), laov[["grupo:Cor.Raca"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| vocab.pre |
1 |
74 |
83.114 |
0.000 |
* |
0.529 |
| grupo |
1 |
74 |
0.321 |
0.573 |
|
0.004 |
| Cor.Raca |
1 |
74 |
3.535 |
0.064 |
|
0.046 |
| grupo:Cor.Raca |
1 |
74 |
1.797 |
0.184 |
|
0.024 |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwcs <- list()
pwcs[["Cor.Raca"]] <- emmeans_test(
group_by(wdat, grupo), vocab.pos ~ Cor.Raca,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Cor.Raca), vocab.pos ~ grupo,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
|
Parda |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
74 |
0.105 |
0.916 |
0.916 |
ns |
|
Branca |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
74 |
-1.451 |
0.151 |
0.151 |
ns |
| Controle |
|
vocab.pre*Cor.Raca |
vocab.pos |
Parda |
Branca |
74 |
-0.822 |
0.414 |
0.414 |
ns |
| Experimental |
|
vocab.pre*Cor.Raca |
vocab.pos |
Parda |
Branca |
74 |
-2.158 |
0.034 |
0.034 |
* |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Cor.Raca")),
vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Cor.Raca"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Cor.Raca"]],
by=c("grupo","Cor.Raca","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Parda |
time |
vocab |
pre |
pos |
150 |
-0.283 |
0.778 |
0.778 |
ns |
| Controle |
Branca |
time |
vocab |
pre |
pos |
150 |
-0.545 |
0.586 |
0.586 |
ns |
| Experimental |
Parda |
time |
vocab |
pre |
pos |
150 |
-0.277 |
0.782 |
0.782 |
ns |
| Experimental |
Branca |
time |
vocab |
pre |
pos |
150 |
-1.375 |
0.171 |
0.171 |
ns |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.pos", c("grupo","Cor.Raca"), covar = "vocab.pre")
ds <- merge(ds[ds$variable != "vocab.pre",],
ds[ds$variable == "vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Cor.Raca","n","mean.vocab.pre","se.vocab.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Cor.Raca"]] <- merge(ds, lemms[["grupo:Cor.Raca"]],
by=c("grupo","Cor.Raca"), suffixes = c("","'"))
}
| Controle |
Branca |
11 |
23.909 |
1.919 |
25.727 |
2.501 |
24.831 |
1.671 |
21.502 |
28.160 |
| Controle |
Parda |
45 |
23.156 |
1.120 |
23.622 |
1.178 |
23.301 |
0.825 |
21.656 |
24.945 |
| Experimental |
Branca |
5 |
21.200 |
2.478 |
28.000 |
3.332 |
29.171 |
2.477 |
24.235 |
34.106 |
| Experimental |
Parda |
18 |
21.389 |
2.104 |
22.111 |
1.937 |
23.138 |
1.309 |
20.530 |
25.745 |
Plots for ancova
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Cor.Raca", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggPlotAoC2(pwcs, "Cor.Raca", "grupo", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.pos", c("grupo","Cor.Raca"), aov, pwcs, covar = "vocab.pre",
theme = "classic", color = color[["grupo:Cor.Raca"]],
subtitle = which(aov$Effect == "grupo:Cor.Raca"))
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots[["grupo:Cor.Raca"]] + ggplot2::ylab("Vocabulary") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab", c("grupo","Cor.Raca"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
plots[["grupo:Cor.Raca"]] + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
facet.by = c("grupo","Cor.Raca"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
color = "grupo", facet.by = "Cor.Raca", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
color = "Cor.Raca", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Cor.Raca)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
res <- augment(lm(vocab.pos ~ vocab.pre + grupo*Cor.Raca, data = wdat))
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.993 0.940
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
levene_test(res, .resid ~ grupo*Cor.Raca)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 75 0.143 0.934
ANCOVA and
Pairwise for two factors grupo:Serie
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Serie"]]),],
"vocab.pos", c("grupo","Serie"))
pdat = pdat[pdat[["Serie"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Serie"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Serie"]] = factor(
pdat[["Serie"]],
level[["Serie"]][level[["Serie"]] %in% unique(pdat[["Serie"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Serie")], pdat[,c("id","grupo","Serie")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab"]] <- c(pdat[["vocab.pre"]], pdat[["vocab.pos"]])
if (length(unique(pdat[["Serie"]])) >= 2) {
aov = anova_test(pdat, vocab.pos ~ vocab.pre + grupo*Serie)
laov[["grupo:Serie"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
pwcs <- list()
pwcs[["Serie"]] <- emmeans_test(
group_by(pdat, grupo), vocab.pos ~ Serie,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Serie), vocab.pos ~ grupo,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
if (length(unique(pdat[["Serie"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Serie")),
vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Serie"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.pos", c("grupo","Serie"), covar = "vocab.pre")
ds <- merge(ds[ds$variable != "vocab.pre",],
ds[ds$variable == "vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Serie"), all.x = T, suffixes = c("", ".vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Serie"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Serie","n","mean.vocab.pre","se.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Serie"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Serie"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.pos ~ vocab.pre + grupo*Serie, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Serie")], wdat[,c("id","grupo","Serie")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab"]] <- c(wdat[["vocab.pre"]], wdat[["vocab.pos"]])
ldat[["grupo:Serie"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Serie"]])) >= 2) {
aov = anova_test(wdat, vocab.pos ~ vocab.pre + grupo*Serie)
laov[["grupo:Serie"]] <- merge(get_anova_table(aov), laov[["grupo:Serie"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| vocab.pre |
1 |
137 |
80.486 |
0.000 |
* |
0.370 |
| grupo |
1 |
137 |
0.083 |
0.774 |
|
0.001 |
| Serie |
3 |
137 |
1.966 |
0.122 |
|
0.041 |
| grupo:Serie |
3 |
137 |
1.941 |
0.126 |
|
0.041 |
if (length(unique(pdat[["Serie"]])) >= 2) {
pwcs <- list()
pwcs[["Serie"]] <- emmeans_test(
group_by(wdat, grupo), vocab.pos ~ Serie,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Serie), vocab.pos ~ grupo,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
|
6 ano |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
137 |
-1.397 |
0.165 |
0.165 |
ns |
|
7 ano |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
137 |
1.786 |
0.076 |
0.076 |
ns |
|
8 ano |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
137 |
-0.843 |
0.401 |
0.401 |
ns |
|
9 ano |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
137 |
-0.182 |
0.855 |
0.855 |
ns |
| Controle |
|
vocab.pre*Serie |
vocab.pos |
6 ano |
7 ano |
137 |
-3.114 |
0.002 |
0.013 |
* |
| Controle |
|
vocab.pre*Serie |
vocab.pos |
6 ano |
8 ano |
137 |
-1.238 |
0.218 |
1.000 |
ns |
| Controle |
|
vocab.pre*Serie |
vocab.pos |
6 ano |
9 ano |
137 |
-2.085 |
0.039 |
0.234 |
ns |
| Controle |
|
vocab.pre*Serie |
vocab.pos |
7 ano |
8 ano |
137 |
1.523 |
0.130 |
0.780 |
ns |
| Controle |
|
vocab.pre*Serie |
vocab.pos |
7 ano |
9 ano |
137 |
0.854 |
0.394 |
1.000 |
ns |
| Controle |
|
vocab.pre*Serie |
vocab.pos |
8 ano |
9 ano |
137 |
-0.729 |
0.467 |
1.000 |
ns |
| Experimental |
|
vocab.pre*Serie |
vocab.pos |
6 ano |
7 ano |
137 |
0.551 |
0.583 |
1.000 |
ns |
| Experimental |
|
vocab.pre*Serie |
vocab.pos |
6 ano |
8 ano |
137 |
-0.569 |
0.570 |
1.000 |
ns |
| Experimental |
|
vocab.pre*Serie |
vocab.pos |
6 ano |
9 ano |
137 |
-0.486 |
0.628 |
1.000 |
ns |
| Experimental |
|
vocab.pre*Serie |
vocab.pos |
7 ano |
8 ano |
137 |
-1.130 |
0.260 |
1.000 |
ns |
| Experimental |
|
vocab.pre*Serie |
vocab.pos |
7 ano |
9 ano |
137 |
-0.968 |
0.335 |
1.000 |
ns |
| Experimental |
|
vocab.pre*Serie |
vocab.pos |
8 ano |
9 ano |
137 |
0.001 |
1.000 |
1.000 |
ns |
if (length(unique(pdat[["Serie"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Serie")),
vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Serie"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Serie"]],
by=c("grupo","Serie","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
6 ano |
time |
vocab |
pre |
pos |
276 |
0.357 |
0.721 |
0.721 |
ns |
| Controle |
7 ano |
time |
vocab |
pre |
pos |
276 |
-1.611 |
0.108 |
0.108 |
ns |
| Controle |
8 ano |
time |
vocab |
pre |
pos |
276 |
-0.046 |
0.963 |
0.963 |
ns |
| Controle |
9 ano |
time |
vocab |
pre |
pos |
276 |
0.147 |
0.883 |
0.883 |
ns |
| Experimental |
6 ano |
time |
vocab |
pre |
pos |
276 |
-0.398 |
0.691 |
0.691 |
ns |
| Experimental |
7 ano |
time |
vocab |
pre |
pos |
276 |
0.212 |
0.832 |
0.832 |
ns |
| Experimental |
8 ano |
time |
vocab |
pre |
pos |
276 |
-0.896 |
0.371 |
0.371 |
ns |
| Experimental |
9 ano |
time |
vocab |
pre |
pos |
276 |
-0.406 |
0.685 |
0.685 |
ns |
if (length(unique(pdat[["Serie"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.pos", c("grupo","Serie"), covar = "vocab.pre")
ds <- merge(ds[ds$variable != "vocab.pre",],
ds[ds$variable == "vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Serie"), all.x = T, suffixes = c("", ".vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Serie"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Serie","n","mean.vocab.pre","se.vocab.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Serie"]] <- merge(ds, lemms[["grupo:Serie"]],
by=c("grupo","Serie"), suffixes = c("","'"))
}
| Controle |
6 ano |
26 |
19.423 |
1.089 |
18.692 |
0.979 |
21.510 |
1.236 |
19.066 |
23.953 |
| Controle |
7 ano |
28 |
23.286 |
1.129 |
26.464 |
1.549 |
26.753 |
1.152 |
24.474 |
29.031 |
| Controle |
8 ano |
17 |
23.882 |
2.592 |
24.000 |
2.329 |
23.898 |
1.478 |
20.975 |
26.820 |
| Controle |
9 ano |
27 |
29.148 |
1.253 |
28.852 |
1.475 |
25.301 |
1.238 |
22.854 |
27.749 |
| Experimental |
6 ano |
13 |
22.385 |
1.960 |
23.538 |
1.742 |
24.417 |
1.693 |
21.069 |
27.765 |
| Experimental |
7 ano |
13 |
23.692 |
2.129 |
23.077 |
2.330 |
23.099 |
1.690 |
19.757 |
26.441 |
| Experimental |
8 ano |
14 |
22.357 |
2.247 |
24.857 |
2.323 |
25.753 |
1.632 |
22.527 |
28.980 |
| Experimental |
9 ano |
8 |
25.250 |
1.221 |
26.750 |
2.448 |
25.752 |
2.157 |
21.486 |
30.018 |
Plots for ancova
if (length(unique(pdat[["Serie"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Serie", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Serie"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
ggPlotAoC2(pwcs, "Serie", "grupo", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.pos", c("grupo","Serie"), aov, pwcs, covar = "vocab.pre",
theme = "classic", color = color[["grupo:Serie"]],
subtitle = which(aov$Effect == "grupo:Serie"))
}
if (length(unique(pdat[["Serie"]])) >= 2) {
plots[["grupo:Serie"]] + ggplot2::ylab("Vocabulary") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab", c("grupo","Serie"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Serie"]])) >= 2)
plots[["grupo:Serie"]] + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
facet.by = c("grupo","Serie"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
color = "grupo", facet.by = "Serie", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
color = "Serie", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Serie)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
ggplot2::scale_color_manual(values = color[["Serie"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Serie"]])) >= 2)
res <- augment(lm(vocab.pos ~ vocab.pre + grupo*Serie, data = wdat))
if (length(unique(pdat[["Serie"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.987 0.191
if (length(unique(pdat[["Serie"]])) >= 2)
levene_test(res, .resid ~ grupo*Serie)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 138 1.12 0.354
ANCOVA
and Pairwise for two factors grupo:vocab.quintile
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["vocab.quintile"]]),],
"vocab.pos", c("grupo","vocab.quintile"))
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
pdat = pdat[pdat[["vocab.quintile"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["vocab.quintile"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["vocab.quintile"]] = factor(
pdat[["vocab.quintile"]],
level[["vocab.quintile"]][level[["vocab.quintile"]] %in% unique(pdat[["vocab.quintile"]])])
pdat.long <- rbind(pdat[,c("id","grupo","vocab.quintile")], pdat[,c("id","grupo","vocab.quintile")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab"]] <- c(pdat[["vocab.pre"]], pdat[["vocab.pos"]])
if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
aov = anova_test(pdat, vocab.pos ~ vocab.pre + grupo*vocab.quintile)
laov[["grupo:vocab.quintile"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["vocab.quintile"]] <- emmeans_test(
group_by(pdat, grupo), vocab.pos ~ vocab.quintile,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, vocab.quintile), vocab.pos ~ grupo,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["vocab.quintile"]])
pwc <- pwc[,c("grupo","vocab.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","vocab.quintile")])]
}
if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","vocab.quintile")),
vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:vocab.quintile"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.pos", c("grupo","vocab.quintile"), covar = "vocab.pre")
ds <- merge(ds[ds$variable != "vocab.pre",],
ds[ds$variable == "vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","vocab.quintile"), all.x = T, suffixes = c("", ".vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","vocab.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","vocab.quintile","n","mean.vocab.pre","se.vocab.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","vocab.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:vocab.quintile"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.pos ~ vocab.pre + grupo*vocab.quintile, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","vocab.quintile")], wdat[,c("id","grupo","vocab.quintile")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab"]] <- c(wdat[["vocab.pre"]], wdat[["vocab.pos"]])
ldat[["grupo:vocab.quintile"]] = wdat
(non.normal)
}
## [1] "P3569"
if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
aov = anova_test(wdat, vocab.pos ~ vocab.pre + grupo*vocab.quintile)
laov[["grupo:vocab.quintile"]] <- merge(get_anova_table(aov), laov[["grupo:vocab.quintile"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| vocab.pre |
1 |
122 |
5.619 |
0.019 |
* |
0.044 |
| grupo |
1 |
122 |
0.003 |
0.959 |
|
0.000 |
| vocab.quintile |
3 |
122 |
0.702 |
0.553 |
|
0.017 |
| grupo:vocab.quintile |
3 |
122 |
1.797 |
0.151 |
|
0.042 |
if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["vocab.quintile"]] <- emmeans_test(
group_by(wdat, grupo), vocab.pos ~ vocab.quintile,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, vocab.quintile), vocab.pos ~ grupo,
covariate = vocab.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["vocab.quintile"]])
pwc <- pwc[,c("grupo","vocab.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","vocab.quintile")])]
}
|
1st quintile |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
122 |
-0.383 |
0.703 |
0.703 |
ns |
|
2nd quintile |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
122 |
0.593 |
0.554 |
0.554 |
ns |
|
3rd quintile |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
122 |
-1.322 |
0.188 |
0.188 |
ns |
|
4th quintile |
vocab.pre*grupo |
vocab.pos |
Controle |
Experimental |
122 |
1.771 |
0.079 |
0.079 |
ns |
| Controle |
|
vocab.pre*vocab.quintile |
vocab.pos |
1st quintile |
2nd quintile |
122 |
0.147 |
0.883 |
1.000 |
ns |
| Controle |
|
vocab.pre*vocab.quintile |
vocab.pos |
1st quintile |
3rd quintile |
122 |
-0.200 |
0.842 |
1.000 |
ns |
| Controle |
|
vocab.pre*vocab.quintile |
vocab.pos |
1st quintile |
4th quintile |
122 |
-0.832 |
0.407 |
1.000 |
ns |
| Controle |
|
vocab.pre*vocab.quintile |
vocab.pos |
2nd quintile |
3rd quintile |
122 |
-0.465 |
0.643 |
1.000 |
ns |
| Controle |
|
vocab.pre*vocab.quintile |
vocab.pos |
2nd quintile |
4th quintile |
122 |
-1.213 |
0.228 |
1.000 |
ns |
| Controle |
|
vocab.pre*vocab.quintile |
vocab.pos |
3rd quintile |
4th quintile |
122 |
-1.399 |
0.164 |
0.985 |
ns |
| Experimental |
|
vocab.pre*vocab.quintile |
vocab.pos |
1st quintile |
2nd quintile |
122 |
0.789 |
0.432 |
1.000 |
ns |
| Experimental |
|
vocab.pre*vocab.quintile |
vocab.pos |
1st quintile |
3rd quintile |
122 |
-0.482 |
0.631 |
1.000 |
ns |
| Experimental |
|
vocab.pre*vocab.quintile |
vocab.pos |
1st quintile |
4th quintile |
122 |
0.230 |
0.818 |
1.000 |
ns |
| Experimental |
|
vocab.pre*vocab.quintile |
vocab.pos |
2nd quintile |
3rd quintile |
122 |
-1.606 |
0.111 |
0.665 |
ns |
| Experimental |
|
vocab.pre*vocab.quintile |
vocab.pos |
2nd quintile |
4th quintile |
122 |
-0.312 |
0.756 |
1.000 |
ns |
| Experimental |
|
vocab.pre*vocab.quintile |
vocab.pos |
3rd quintile |
4th quintile |
122 |
1.188 |
0.237 |
1.000 |
ns |
if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","vocab.quintile")),
vocab ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:vocab.quintile"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:vocab.quintile"]],
by=c("grupo","vocab.quintile","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
1st quintile |
time |
vocab |
pre |
pos |
246 |
-2.565 |
0.011 |
0.011 |
* |
| Controle |
2nd quintile |
time |
vocab |
pre |
pos |
246 |
-0.986 |
0.325 |
0.325 |
ns |
| Controle |
3rd quintile |
time |
vocab |
pre |
pos |
246 |
0.445 |
0.657 |
0.657 |
ns |
| Controle |
4th quintile |
time |
vocab |
pre |
pos |
246 |
-0.441 |
0.660 |
0.660 |
ns |
| Experimental |
1st quintile |
time |
vocab |
pre |
pos |
246 |
-2.589 |
0.010 |
0.010 |
* |
| Experimental |
2nd quintile |
time |
vocab |
pre |
pos |
246 |
0.058 |
0.953 |
0.953 |
ns |
| Experimental |
3rd quintile |
time |
vocab |
pre |
pos |
246 |
-1.478 |
0.141 |
0.141 |
ns |
| Experimental |
4th quintile |
time |
vocab |
pre |
pos |
246 |
1.818 |
0.070 |
0.070 |
ns |
if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.pos", c("grupo","vocab.quintile"), covar = "vocab.pre")
ds <- merge(ds[ds$variable != "vocab.pre",],
ds[ds$variable == "vocab.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","vocab.quintile"), all.x = T, suffixes = c("", ".vocab.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","vocab.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","vocab.quintile","n","mean.vocab.pre","se.vocab.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","vocab.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:vocab.quintile"]] <- merge(ds, lemms[["grupo:vocab.quintile"]],
by=c("grupo","vocab.quintile"), suffixes = c("","'"))
}
| Controle |
1st quintile |
17 |
13.529 |
0.550 |
17.294 |
0.915 |
22.588 |
2.616 |
17.411 |
27.766 |
| Controle |
2nd quintile |
27 |
19.296 |
0.287 |
20.444 |
1.022 |
22.254 |
1.323 |
19.636 |
24.872 |
| Controle |
3rd quintile |
31 |
26.097 |
0.389 |
25.613 |
1.079 |
23.312 |
1.399 |
20.543 |
26.082 |
| Controle |
4th quintile |
9 |
31.667 |
0.471 |
32.556 |
2.416 |
26.889 |
3.035 |
20.880 |
32.898 |
| Experimental |
1st quintile |
9 |
12.111 |
0.964 |
17.333 |
2.279 |
23.485 |
3.199 |
17.152 |
29.817 |
| Experimental |
2nd quintile |
8 |
19.125 |
0.639 |
19.000 |
2.188 |
20.913 |
2.142 |
16.672 |
25.153 |
| Experimental |
3rd quintile |
21 |
25.286 |
0.432 |
27.238 |
1.322 |
25.428 |
1.443 |
22.571 |
28.285 |
| Experimental |
4th quintile |
9 |
31.333 |
0.527 |
27.667 |
1.581 |
22.202 |
2.969 |
16.324 |
28.079 |
Plots for ancova
if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "vocab.quintile", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:vocab.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["vocab.quintile"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "vocab.quintile", "grupo", aov, ylab = "Vocabulary",
subtitle = which(aov$Effect == "grupo:vocab.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.pos", c("grupo","vocab.quintile"), aov, pwcs, covar = "vocab.pre",
theme = "classic", color = color[["grupo:vocab.quintile"]],
subtitle = which(aov$Effect == "grupo:vocab.quintile"))
}
if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
plots[["grupo:vocab.quintile"]] + ggplot2::ylab("Vocabulary") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab", c("grupo","vocab.quintile"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["vocab.quintile"]])) >= 2)
plots[["grupo:vocab.quintile"]] + ggplot2::ylab("Vocabulary") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
facet.by = c("grupo","vocab.quintile"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
color = "grupo", facet.by = "vocab.quintile", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:vocab.quintile"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["vocab.quintile"]])) >= 2) {
ggscatter(wdat, x = "vocab.pre", y = "vocab.pos", size = 0.5,
color = "vocab.quintile", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = vocab.quintile)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:vocab.quintile"))) +
ggplot2::scale_color_manual(values = color[["vocab.quintile"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["vocab.quintile"]])) >= 2)
res <- augment(lm(vocab.pos ~ vocab.pre + grupo*vocab.quintile, data = wdat))
if (length(unique(pdat[["vocab.quintile"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.988 0.294
if (length(unique(pdat[["vocab.quintile"]])) >= 2)
levene_test(res, .resid ~ grupo*vocab.quintile)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 123 0.203 0.984
Summary of Results
Descriptive Statistics
df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
|
vocab.pre |
97 |
24.031 |
23.0 |
7 |
45 |
7.848 |
0.797 |
1.582 |
10.00 |
YES |
0.372 |
-0.373 |
| Experimental |
|
|
|
|
|
vocab.pre |
48 |
23.208 |
24.0 |
7 |
39 |
7.092 |
1.024 |
2.059 |
9.50 |
YES |
-0.315 |
-0.462 |
|
|
|
|
|
|
vocab.pre |
145 |
23.759 |
24.0 |
7 |
45 |
7.591 |
0.630 |
1.246 |
10.00 |
YES |
0.205 |
-0.274 |
| Controle |
|
|
|
|
|
vocab.pos |
97 |
24.402 |
23.0 |
11 |
45 |
8.186 |
0.831 |
1.650 |
11.00 |
NO |
0.539 |
-0.535 |
| Experimental |
|
|
|
|
|
vocab.pos |
48 |
24.333 |
24.5 |
8 |
40 |
7.603 |
1.097 |
2.208 |
10.25 |
YES |
-0.015 |
-0.642 |
|
|
|
|
|
|
vocab.pos |
145 |
24.379 |
23.0 |
8 |
45 |
7.971 |
0.662 |
1.308 |
10.00 |
YES |
0.387 |
-0.516 |
| Controle |
F |
|
|
|
|
vocab.pre |
43 |
24.535 |
25.0 |
12 |
36 |
6.874 |
1.048 |
2.116 |
9.00 |
YES |
-0.003 |
-1.003 |
| Controle |
M |
|
|
|
|
vocab.pre |
54 |
23.630 |
22.5 |
7 |
45 |
8.588 |
1.169 |
2.344 |
12.00 |
NO |
0.561 |
-0.280 |
| Experimental |
F |
|
|
|
|
vocab.pre |
16 |
24.000 |
25.5 |
11 |
33 |
6.976 |
1.744 |
3.717 |
7.75 |
NO |
-0.631 |
-0.789 |
| Experimental |
M |
|
|
|
|
vocab.pre |
32 |
22.812 |
23.5 |
7 |
39 |
7.226 |
1.277 |
2.605 |
10.25 |
YES |
-0.148 |
-0.386 |
| Controle |
F |
|
|
|
|
vocab.pos |
43 |
24.907 |
23.0 |
14 |
40 |
6.938 |
1.058 |
2.135 |
7.50 |
NO |
0.698 |
-0.419 |
| Controle |
M |
|
|
|
|
vocab.pos |
54 |
24.000 |
22.5 |
11 |
45 |
9.103 |
1.239 |
2.485 |
12.75 |
NO |
0.512 |
-0.782 |
| Experimental |
F |
|
|
|
|
vocab.pos |
16 |
24.062 |
23.0 |
17 |
40 |
5.767 |
1.442 |
3.073 |
9.00 |
NO |
1.055 |
1.067 |
| Experimental |
M |
|
|
|
|
vocab.pos |
32 |
24.469 |
25.5 |
8 |
39 |
8.455 |
1.495 |
3.048 |
13.00 |
YES |
-0.210 |
-1.095 |
| Controle |
|
Rural |
|
|
|
vocab.pre |
55 |
23.691 |
23.0 |
12 |
44 |
7.102 |
0.958 |
1.920 |
10.00 |
YES |
0.353 |
-0.368 |
| Controle |
|
Urbana |
|
|
|
vocab.pre |
11 |
22.455 |
20.0 |
7 |
45 |
10.539 |
3.178 |
7.080 |
9.50 |
NO |
0.669 |
-0.409 |
| Experimental |
|
Rural |
|
|
|
vocab.pre |
34 |
22.618 |
24.0 |
7 |
39 |
7.274 |
1.247 |
2.538 |
8.50 |
YES |
-0.208 |
-0.361 |
| Experimental |
|
Urbana |
|
|
|
vocab.pre |
5 |
26.200 |
26.0 |
15 |
33 |
7.463 |
3.338 |
9.267 |
9.00 |
YES |
-0.379 |
-1.708 |
| Controle |
|
Rural |
|
|
|
vocab.pos |
55 |
24.418 |
23.0 |
11 |
45 |
8.182 |
1.103 |
2.212 |
11.00 |
YES |
0.365 |
-0.536 |
| Controle |
|
Urbana |
|
|
|
vocab.pos |
11 |
22.091 |
20.0 |
12 |
41 |
8.166 |
2.462 |
5.486 |
7.50 |
NO |
0.985 |
0.027 |
| Experimental |
|
Rural |
|
|
|
vocab.pos |
34 |
24.059 |
24.0 |
8 |
39 |
7.847 |
1.346 |
2.738 |
10.75 |
YES |
-0.142 |
-0.792 |
| Experimental |
|
Urbana |
|
|
|
vocab.pos |
5 |
23.400 |
23.0 |
16 |
34 |
7.127 |
3.187 |
8.850 |
8.00 |
YES |
0.357 |
-1.720 |
| Controle |
|
|
Parda |
|
|
vocab.pre |
45 |
23.156 |
23.0 |
12 |
44 |
7.517 |
1.120 |
2.258 |
10.00 |
YES |
0.488 |
-0.335 |
| Controle |
|
|
Branca |
|
|
vocab.pre |
11 |
23.909 |
27.0 |
14 |
31 |
6.363 |
1.919 |
4.275 |
11.00 |
YES |
-0.423 |
-1.709 |
| Experimental |
|
|
Parda |
|
|
vocab.pre |
18 |
21.389 |
22.0 |
7 |
39 |
8.925 |
2.104 |
4.439 |
12.75 |
YES |
0.068 |
-1.053 |
| Experimental |
|
|
Branca |
|
|
vocab.pre |
5 |
21.200 |
23.0 |
15 |
28 |
5.541 |
2.478 |
6.880 |
8.00 |
YES |
-0.043 |
-2.062 |
| Controle |
|
|
Parda |
|
|
vocab.pos |
45 |
23.622 |
23.0 |
11 |
45 |
7.904 |
1.178 |
2.375 |
7.00 |
NO |
0.612 |
-0.003 |
| Controle |
|
|
Branca |
|
|
vocab.pos |
11 |
25.727 |
27.0 |
14 |
40 |
8.296 |
2.501 |
5.573 |
13.00 |
YES |
0.140 |
-1.465 |
| Experimental |
|
|
Parda |
|
|
vocab.pos |
18 |
22.111 |
21.0 |
8 |
39 |
8.217 |
1.937 |
4.086 |
11.50 |
YES |
0.331 |
-0.790 |
| Experimental |
|
|
Branca |
|
|
vocab.pos |
5 |
28.000 |
31.0 |
16 |
35 |
7.450 |
3.332 |
9.250 |
6.00 |
NO |
-0.630 |
-1.475 |
| Controle |
|
|
|
6 ano |
|
vocab.pre |
26 |
19.423 |
18.5 |
12 |
29 |
5.551 |
1.089 |
2.242 |
8.50 |
YES |
0.275 |
-1.373 |
| Controle |
|
|
|
7 ano |
|
vocab.pre |
28 |
23.286 |
23.0 |
12 |
35 |
5.974 |
1.129 |
2.317 |
9.00 |
YES |
0.142 |
-0.813 |
| Controle |
|
|
|
8 ano |
|
vocab.pre |
17 |
23.882 |
19.0 |
7 |
45 |
10.688 |
2.592 |
5.495 |
16.00 |
NO |
0.658 |
-0.739 |
| Controle |
|
|
|
9 ano |
|
vocab.pre |
27 |
29.148 |
29.0 |
13 |
42 |
6.509 |
1.253 |
2.575 |
9.50 |
YES |
-0.370 |
-0.275 |
| Experimental |
|
|
|
6 ano |
|
vocab.pre |
13 |
22.385 |
24.0 |
9 |
30 |
7.066 |
1.960 |
4.270 |
10.00 |
NO |
-0.583 |
-1.102 |
| Experimental |
|
|
|
7 ano |
|
vocab.pre |
13 |
23.692 |
23.0 |
13 |
39 |
7.674 |
2.129 |
4.638 |
8.00 |
YES |
0.455 |
-0.930 |
| Experimental |
|
|
|
8 ano |
|
vocab.pre |
14 |
22.357 |
23.5 |
7 |
33 |
8.409 |
2.247 |
4.855 |
14.25 |
YES |
-0.363 |
-1.307 |
| Experimental |
|
|
|
9 ano |
|
vocab.pre |
8 |
25.250 |
25.0 |
19 |
29 |
3.454 |
1.221 |
2.887 |
4.50 |
YES |
-0.403 |
-1.235 |
| Controle |
|
|
|
6 ano |
|
vocab.pos |
26 |
18.692 |
18.5 |
11 |
27 |
4.994 |
0.979 |
2.017 |
8.25 |
YES |
0.037 |
-1.282 |
| Controle |
|
|
|
7 ano |
|
vocab.pos |
28 |
26.464 |
24.0 |
12 |
47 |
8.194 |
1.549 |
3.177 |
8.50 |
NO |
0.644 |
-0.211 |
| Controle |
|
|
|
8 ano |
|
vocab.pos |
17 |
24.000 |
21.0 |
14 |
45 |
9.605 |
2.329 |
4.938 |
11.00 |
NO |
0.940 |
-0.562 |
| Controle |
|
|
|
9 ano |
|
vocab.pos |
27 |
28.852 |
29.0 |
14 |
41 |
7.665 |
1.475 |
3.032 |
10.00 |
YES |
-0.076 |
-1.152 |
| Experimental |
|
|
|
6 ano |
|
vocab.pos |
13 |
23.538 |
25.0 |
14 |
35 |
6.280 |
1.742 |
3.795 |
9.00 |
YES |
-0.102 |
-1.103 |
| Experimental |
|
|
|
7 ano |
|
vocab.pos |
13 |
23.077 |
22.0 |
10 |
39 |
8.401 |
2.330 |
5.077 |
10.00 |
YES |
0.127 |
-1.001 |
| Experimental |
|
|
|
8 ano |
|
vocab.pos |
14 |
24.857 |
25.0 |
8 |
37 |
8.690 |
2.323 |
5.017 |
13.25 |
YES |
-0.248 |
-1.225 |
| Experimental |
|
|
|
9 ano |
|
vocab.pos |
8 |
26.750 |
25.5 |
18 |
40 |
6.923 |
2.448 |
5.788 |
7.25 |
NO |
0.589 |
-0.890 |
| Controle |
|
|
|
|
1st quintile |
vocab.pre |
17 |
13.529 |
14.0 |
7 |
16 |
2.267 |
0.550 |
1.166 |
3.00 |
NO |
-1.142 |
1.450 |
| Controle |
|
|
|
|
2nd quintile |
vocab.pre |
27 |
19.296 |
19.0 |
17 |
22 |
1.489 |
0.287 |
0.589 |
1.00 |
YES |
0.248 |
-0.714 |
| Controle |
|
|
|
|
3rd quintile |
vocab.pre |
31 |
26.097 |
27.0 |
23 |
29 |
2.166 |
0.389 |
0.794 |
4.00 |
YES |
-0.216 |
-1.517 |
| Controle |
|
|
|
|
4th quintile |
vocab.pre |
9 |
31.667 |
31.0 |
30 |
34 |
1.414 |
0.471 |
1.087 |
2.00 |
YES |
0.288 |
-1.556 |
| Experimental |
|
|
|
|
1st quintile |
vocab.pre |
9 |
12.111 |
13.0 |
7 |
16 |
2.892 |
0.964 |
2.223 |
3.00 |
YES |
-0.346 |
-1.265 |
| Experimental |
|
|
|
|
2nd quintile |
vocab.pre |
8 |
19.125 |
19.0 |
17 |
22 |
1.808 |
0.639 |
1.511 |
2.50 |
YES |
0.220 |
-1.553 |
| Experimental |
|
|
|
|
3rd quintile |
vocab.pre |
21 |
25.286 |
25.0 |
23 |
29 |
1.978 |
0.432 |
0.901 |
2.00 |
NO |
0.544 |
-1.059 |
| Experimental |
|
|
|
|
4th quintile |
vocab.pre |
9 |
31.333 |
30.0 |
30 |
33 |
1.581 |
0.527 |
1.215 |
3.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
1st quintile |
vocab.pos |
17 |
17.294 |
18.0 |
11 |
23 |
3.771 |
0.915 |
1.939 |
5.00 |
YES |
-0.367 |
-1.195 |
| Controle |
|
|
|
|
2nd quintile |
vocab.pos |
27 |
20.444 |
21.0 |
12 |
33 |
5.308 |
1.022 |
2.100 |
6.50 |
YES |
0.331 |
-0.467 |
| Controle |
|
|
|
|
3rd quintile |
vocab.pos |
31 |
25.613 |
25.0 |
14 |
40 |
6.009 |
1.079 |
2.204 |
4.50 |
YES |
0.435 |
0.073 |
| Controle |
|
|
|
|
4th quintile |
vocab.pos |
9 |
32.556 |
32.0 |
16 |
40 |
7.248 |
2.416 |
5.571 |
7.00 |
NO |
-1.053 |
0.290 |
| Experimental |
|
|
|
|
1st quintile |
vocab.pos |
9 |
17.333 |
16.0 |
8 |
31 |
6.837 |
2.279 |
5.256 |
4.00 |
NO |
0.611 |
-0.655 |
| Experimental |
|
|
|
|
2nd quintile |
vocab.pos |
8 |
19.000 |
19.5 |
10 |
30 |
6.188 |
2.188 |
5.173 |
6.75 |
YES |
0.260 |
-1.092 |
| Experimental |
|
|
|
|
3rd quintile |
vocab.pos |
21 |
27.238 |
26.0 |
18 |
40 |
6.057 |
1.322 |
2.757 |
9.00 |
YES |
0.433 |
-0.869 |
| Experimental |
|
|
|
|
4th quintile |
vocab.pos |
9 |
27.667 |
28.0 |
19 |
34 |
4.743 |
1.581 |
3.646 |
5.00 |
YES |
-0.365 |
-1.109 |
ANCOVA Table Comparison
df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
| 1 |
grupo |
1 |
142 |
0.258 |
0.612 |
|
0.002 |
1 |
143 |
0.050 |
0.824 |
|
0.000 |
| 2 |
vocab.pre |
1 |
142 |
126.192 |
0.000 |
* |
0.471 |
1 |
143 |
106.978 |
0.000 |
* |
0.428 |
| 4 |
grupo:Sexo |
1 |
140 |
0.493 |
0.484 |
|
0.004 |
1 |
141 |
0.203 |
0.653 |
|
0.001 |
| 5 |
Sexo |
1 |
140 |
0.046 |
0.831 |
|
0.000 |
1 |
141 |
0.248 |
0.619 |
|
0.002 |
| 8 |
grupo:Zona |
1 |
100 |
0.538 |
0.465 |
|
0.005 |
1 |
101 |
0.241 |
0.624 |
|
0.002 |
| 10 |
Zona |
1 |
100 |
2.086 |
0.152 |
|
0.020 |
1 |
101 |
2.238 |
0.138 |
|
0.022 |
| 11 |
Cor.Raca |
1 |
74 |
3.535 |
0.064 |
|
0.046 |
1 |
74 |
3.535 |
0.064 |
|
0.046 |
| 13 |
grupo:Cor.Raca |
1 |
74 |
1.797 |
0.184 |
|
0.024 |
1 |
74 |
1.797 |
0.184 |
|
0.024 |
| 16 |
grupo:Serie |
3 |
137 |
1.941 |
0.126 |
|
0.041 |
3 |
137 |
1.941 |
0.126 |
|
0.041 |
| 17 |
Serie |
3 |
137 |
1.966 |
0.122 |
|
0.041 |
3 |
137 |
1.966 |
0.122 |
|
0.041 |
| 20 |
grupo:vocab.quintile |
3 |
122 |
1.797 |
0.151 |
|
0.042 |
3 |
123 |
1.721 |
0.166 |
|
0.040 |
| 22 |
vocab.quintile |
3 |
122 |
0.702 |
0.553 |
|
0.017 |
3 |
123 |
0.263 |
0.852 |
|
0.006 |
PairWise Table Comparison
df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
| Controle |
|
|
|
|
|
pre |
pos |
286 |
-0.331 |
0.741 |
0.741 |
ns |
288 |
-0.579 |
0.563 |
0.563 |
ns |
| Experimental |
|
|
|
|
|
pre |
pos |
286 |
-0.706 |
0.481 |
0.481 |
ns |
288 |
-0.698 |
0.486 |
0.486 |
ns |
|
|
|
|
|
|
Controle |
Experimental |
142 |
-0.508 |
0.612 |
0.612 |
ns |
143 |
-0.223 |
0.824 |
0.824 |
ns |
| Controle |
F |
|
|
|
|
pre |
pos |
282 |
-0.220 |
0.826 |
0.826 |
ns |
284 |
-0.217 |
0.828 |
0.828 |
ns |
| Controle |
M |
|
|
|
|
pre |
pos |
282 |
-0.245 |
0.806 |
0.806 |
ns |
284 |
-0.576 |
0.565 |
0.565 |
ns |
| Controle |
|
|
|
|
|
F |
M |
140 |
0.211 |
0.834 |
0.834 |
ns |
141 |
-0.166 |
0.868 |
0.868 |
ns |
| Experimental |
F |
|
|
|
|
pre |
pos |
282 |
-0.023 |
0.982 |
0.982 |
ns |
284 |
-0.022 |
0.982 |
0.982 |
ns |
| Experimental |
M |
|
|
|
|
pre |
pos |
282 |
-0.844 |
0.399 |
0.399 |
ns |
284 |
-0.834 |
0.405 |
0.405 |
ns |
| Experimental |
|
|
|
|
|
F |
M |
140 |
-0.703 |
0.483 |
0.483 |
ns |
141 |
-0.651 |
0.516 |
0.516 |
ns |
|
F |
|
|
|
|
Controle |
Experimental |
140 |
0.266 |
0.790 |
0.790 |
ns |
141 |
0.254 |
0.799 |
0.799 |
ns |
|
M |
|
|
|
|
Controle |
Experimental |
140 |
-0.808 |
0.420 |
0.420 |
ns |
141 |
-0.410 |
0.683 |
0.683 |
ns |
| Controle |
|
|
|
|
|
Rural |
Urbana |
100 |
0.770 |
0.443 |
0.443 |
ns |
101 |
0.952 |
0.343 |
0.343 |
ns |
| Controle |
|
Rural |
|
|
|
pre |
pos |
202 |
-0.488 |
0.626 |
0.626 |
ns |
204 |
-0.810 |
0.419 |
0.419 |
ns |
| Controle |
|
Urbana |
|
|
|
pre |
pos |
202 |
0.109 |
0.913 |
0.913 |
ns |
204 |
0.107 |
0.915 |
0.915 |
ns |
| Experimental |
|
|
|
|
|
Rural |
Urbana |
100 |
1.421 |
0.158 |
0.158 |
ns |
101 |
1.250 |
0.214 |
0.214 |
ns |
| Experimental |
|
Rural |
|
|
|
pre |
pos |
202 |
-0.761 |
0.448 |
0.448 |
ns |
204 |
-0.749 |
0.455 |
0.455 |
ns |
| Experimental |
|
Urbana |
|
|
|
pre |
pos |
202 |
0.567 |
0.571 |
0.571 |
ns |
204 |
0.558 |
0.577 |
0.577 |
ns |
|
|
Rural |
|
|
|
Controle |
Experimental |
100 |
-0.448 |
0.655 |
0.655 |
ns |
101 |
-0.015 |
0.988 |
0.988 |
ns |
|
|
Urbana |
|
|
|
Controle |
Experimental |
100 |
0.612 |
0.542 |
0.542 |
ns |
101 |
0.525 |
0.601 |
0.601 |
ns |
| Controle |
|
|
Branca |
|
|
pre |
pos |
150 |
-0.545 |
0.586 |
0.586 |
ns |
150 |
-0.545 |
0.586 |
0.586 |
ns |
| Controle |
|
|
|
|
|
Parda |
Branca |
74 |
-0.822 |
0.414 |
0.414 |
ns |
74 |
-0.822 |
0.414 |
0.414 |
ns |
| Controle |
|
|
Parda |
|
|
pre |
pos |
150 |
-0.283 |
0.778 |
0.778 |
ns |
150 |
-0.283 |
0.778 |
0.778 |
ns |
| Experimental |
|
|
Branca |
|
|
pre |
pos |
150 |
-1.375 |
0.171 |
0.171 |
ns |
150 |
-1.375 |
0.171 |
0.171 |
ns |
| Experimental |
|
|
|
|
|
Parda |
Branca |
74 |
-2.158 |
0.034 |
0.034 |
* |
74 |
-2.158 |
0.034 |
0.034 |
* |
| Experimental |
|
|
Parda |
|
|
pre |
pos |
150 |
-0.277 |
0.782 |
0.782 |
ns |
150 |
-0.277 |
0.782 |
0.782 |
ns |
|
|
|
Branca |
|
|
Controle |
Experimental |
74 |
-1.451 |
0.151 |
0.151 |
ns |
74 |
-1.451 |
0.151 |
0.151 |
ns |
|
|
|
Parda |
|
|
Controle |
Experimental |
74 |
0.105 |
0.916 |
0.916 |
ns |
74 |
0.105 |
0.916 |
0.916 |
ns |
| Controle |
|
|
|
6 ano |
|
pre |
pos |
276 |
0.357 |
0.721 |
0.721 |
ns |
276 |
0.357 |
0.721 |
0.721 |
ns |
| Controle |
|
|
|
7 ano |
|
pre |
pos |
276 |
-1.611 |
0.108 |
0.108 |
ns |
276 |
-1.611 |
0.108 |
0.108 |
ns |
| Controle |
|
|
|
8 ano |
|
pre |
pos |
276 |
-0.046 |
0.963 |
0.963 |
ns |
276 |
-0.046 |
0.963 |
0.963 |
ns |
| Controle |
|
|
|
9 ano |
|
pre |
pos |
276 |
0.147 |
0.883 |
0.883 |
ns |
276 |
0.147 |
0.883 |
0.883 |
ns |
| Controle |
|
|
|
|
|
6 ano |
7 ano |
137 |
-3.114 |
0.002 |
0.013 |
* |
137 |
-3.114 |
0.002 |
0.013 |
* |
| Controle |
|
|
|
|
|
6 ano |
8 ano |
137 |
-1.238 |
0.218 |
1.000 |
ns |
137 |
-1.238 |
0.218 |
1.000 |
ns |
| Controle |
|
|
|
|
|
6 ano |
9 ano |
137 |
-2.085 |
0.039 |
0.234 |
ns |
137 |
-2.085 |
0.039 |
0.234 |
ns |
| Controle |
|
|
|
|
|
7 ano |
8 ano |
137 |
1.523 |
0.130 |
0.780 |
ns |
137 |
1.523 |
0.130 |
0.780 |
ns |
| Controle |
|
|
|
|
|
7 ano |
9 ano |
137 |
0.854 |
0.394 |
1.000 |
ns |
137 |
0.854 |
0.394 |
1.000 |
ns |
| Controle |
|
|
|
|
|
8 ano |
9 ano |
137 |
-0.729 |
0.467 |
1.000 |
ns |
137 |
-0.729 |
0.467 |
1.000 |
ns |
| Experimental |
|
|
|
6 ano |
|
pre |
pos |
276 |
-0.398 |
0.691 |
0.691 |
ns |
276 |
-0.398 |
0.691 |
0.691 |
ns |
| Experimental |
|
|
|
7 ano |
|
pre |
pos |
276 |
0.212 |
0.832 |
0.832 |
ns |
276 |
0.212 |
0.832 |
0.832 |
ns |
| Experimental |
|
|
|
8 ano |
|
pre |
pos |
276 |
-0.896 |
0.371 |
0.371 |
ns |
276 |
-0.896 |
0.371 |
0.371 |
ns |
| Experimental |
|
|
|
9 ano |
|
pre |
pos |
276 |
-0.406 |
0.685 |
0.685 |
ns |
276 |
-0.406 |
0.685 |
0.685 |
ns |
| Experimental |
|
|
|
|
|
6 ano |
7 ano |
137 |
0.551 |
0.583 |
1.000 |
ns |
137 |
0.551 |
0.583 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
6 ano |
8 ano |
137 |
-0.569 |
0.570 |
1.000 |
ns |
137 |
-0.569 |
0.570 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
6 ano |
9 ano |
137 |
-0.486 |
0.628 |
1.000 |
ns |
137 |
-0.486 |
0.628 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
7 ano |
8 ano |
137 |
-1.130 |
0.260 |
1.000 |
ns |
137 |
-1.130 |
0.260 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
7 ano |
9 ano |
137 |
-0.968 |
0.335 |
1.000 |
ns |
137 |
-0.968 |
0.335 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
8 ano |
9 ano |
137 |
0.001 |
1.000 |
1.000 |
ns |
137 |
0.001 |
1.000 |
1.000 |
ns |
|
|
|
|
6 ano |
|
Controle |
Experimental |
137 |
-1.397 |
0.165 |
0.165 |
ns |
137 |
-1.397 |
0.165 |
0.165 |
ns |
|
|
|
|
7 ano |
|
Controle |
Experimental |
137 |
1.786 |
0.076 |
0.076 |
ns |
137 |
1.786 |
0.076 |
0.076 |
ns |
|
|
|
|
8 ano |
|
Controle |
Experimental |
137 |
-0.843 |
0.401 |
0.401 |
ns |
137 |
-0.843 |
0.401 |
0.401 |
ns |
|
|
|
|
9 ano |
|
Controle |
Experimental |
137 |
-0.182 |
0.855 |
0.855 |
ns |
137 |
-0.182 |
0.855 |
0.855 |
ns |
| Controle |
|
|
|
|
1st quintile |
pre |
pos |
246 |
-2.565 |
0.011 |
0.011 |
* |
248 |
-2.401 |
0.017 |
0.017 |
* |
| Controle |
|
|
|
|
2nd quintile |
pre |
pos |
246 |
-0.986 |
0.325 |
0.325 |
ns |
248 |
-1.724 |
0.086 |
0.086 |
ns |
| Controle |
|
|
|
|
3rd quintile |
pre |
pos |
246 |
0.445 |
0.657 |
0.657 |
ns |
248 |
0.417 |
0.677 |
0.677 |
ns |
| Controle |
|
|
|
|
4th quintile |
pre |
pos |
246 |
-0.441 |
0.660 |
0.660 |
ns |
248 |
-0.412 |
0.680 |
0.680 |
ns |
| Controle |
|
|
|
|
|
1st quintile |
2nd quintile |
122 |
0.147 |
0.883 |
1.000 |
ns |
123 |
-0.290 |
0.772 |
1.000 |
ns |
| Controle |
|
|
|
|
|
1st quintile |
3rd quintile |
122 |
-0.200 |
0.842 |
1.000 |
ns |
123 |
-0.235 |
0.814 |
1.000 |
ns |
| Controle |
|
|
|
|
|
1st quintile |
4th quintile |
122 |
-0.832 |
0.407 |
1.000 |
ns |
123 |
-0.820 |
0.414 |
1.000 |
ns |
| Controle |
|
|
|
|
|
2nd quintile |
3rd quintile |
122 |
-0.465 |
0.643 |
1.000 |
ns |
123 |
-0.086 |
0.932 |
1.000 |
ns |
| Controle |
|
|
|
|
|
2nd quintile |
4th quintile |
122 |
-1.213 |
0.228 |
1.000 |
ns |
123 |
-0.938 |
0.350 |
1.000 |
ns |
| Controle |
|
|
|
|
|
3rd quintile |
4th quintile |
122 |
-1.399 |
0.164 |
0.985 |
ns |
123 |
-1.326 |
0.187 |
1.000 |
ns |
| Experimental |
|
|
|
|
1st quintile |
pre |
pos |
246 |
-2.589 |
0.010 |
0.010 |
* |
248 |
-2.423 |
0.016 |
0.016 |
* |
| Experimental |
|
|
|
|
2nd quintile |
pre |
pos |
246 |
0.058 |
0.953 |
0.953 |
ns |
248 |
0.055 |
0.956 |
0.956 |
ns |
| Experimental |
|
|
|
|
3rd quintile |
pre |
pos |
246 |
-1.478 |
0.141 |
0.141 |
ns |
248 |
-1.384 |
0.168 |
0.168 |
ns |
| Experimental |
|
|
|
|
4th quintile |
pre |
pos |
246 |
1.818 |
0.070 |
0.070 |
ns |
248 |
1.701 |
0.090 |
0.090 |
ns |
| Experimental |
|
|
|
|
|
1st quintile |
2nd quintile |
122 |
0.789 |
0.432 |
1.000 |
ns |
123 |
0.698 |
0.487 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
1st quintile |
3rd quintile |
122 |
-0.482 |
0.631 |
1.000 |
ns |
123 |
-0.493 |
0.623 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
1st quintile |
4th quintile |
122 |
0.230 |
0.818 |
1.000 |
ns |
123 |
0.163 |
0.871 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
2nd quintile |
3rd quintile |
122 |
-1.606 |
0.111 |
0.665 |
ns |
123 |
-1.517 |
0.132 |
0.791 |
ns |
| Experimental |
|
|
|
|
|
2nd quintile |
4th quintile |
122 |
-0.312 |
0.756 |
1.000 |
ns |
123 |
-0.331 |
0.741 |
1.000 |
ns |
| Experimental |
|
|
|
|
|
3rd quintile |
4th quintile |
122 |
1.188 |
0.237 |
1.000 |
ns |
123 |
1.066 |
0.289 |
1.000 |
ns |
|
|
|
|
|
1st quintile |
Controle |
Experimental |
122 |
-0.383 |
0.703 |
0.703 |
ns |
123 |
-0.345 |
0.731 |
0.731 |
ns |
|
|
|
|
|
2nd quintile |
Controle |
Experimental |
122 |
0.593 |
0.554 |
0.554 |
ns |
123 |
0.944 |
0.347 |
0.347 |
ns |
|
|
|
|
|
3rd quintile |
Controle |
Experimental |
122 |
-1.322 |
0.188 |
0.188 |
ns |
123 |
-1.215 |
0.227 |
0.227 |
ns |
|
|
|
|
|
4th quintile |
Controle |
Experimental |
122 |
1.771 |
0.079 |
0.079 |
ns |
123 |
1.639 |
0.104 |
0.104 |
ns |
EMMS Table Comparison
df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% names(lfatores)])]
| Controle |
|
|
|
|
|
97 |
24.031 |
0.797 |
24.402 |
0.831 |
24.206 |
0.593 |
23.033 |
25.379 |
98 |
23.980 |
0.790 |
24.633 |
0.854 |
24.454 |
0.628 |
23.212 |
25.695 |
-1 |
| Experimental |
|
|
|
|
|
48 |
23.208 |
1.024 |
24.333 |
1.097 |
24.730 |
0.844 |
23.062 |
26.398 |
48 |
23.208 |
1.024 |
24.333 |
1.097 |
24.699 |
0.898 |
22.924 |
26.473 |
0 |
| Controle |
F |
|
|
|
|
43 |
24.535 |
1.048 |
24.907 |
1.058 |
24.346 |
0.897 |
22.573 |
26.119 |
43 |
24.535 |
1.048 |
24.907 |
1.058 |
24.334 |
0.955 |
22.447 |
26.221 |
0 |
| Controle |
M |
|
|
|
|
54 |
23.630 |
1.169 |
24.000 |
1.239 |
24.093 |
0.799 |
22.513 |
25.673 |
55 |
23.545 |
1.150 |
24.418 |
1.286 |
24.546 |
0.843 |
22.880 |
26.212 |
-1 |
| Experimental |
F |
|
|
|
|
16 |
24.000 |
1.744 |
24.062 |
1.442 |
23.888 |
1.468 |
20.986 |
26.790 |
16 |
24.000 |
1.744 |
24.062 |
1.442 |
23.869 |
1.562 |
20.780 |
26.957 |
0 |
| Experimental |
M |
|
|
|
|
32 |
22.812 |
1.277 |
24.469 |
1.495 |
25.152 |
1.040 |
23.097 |
27.208 |
32 |
22.812 |
1.277 |
24.469 |
1.495 |
25.116 |
1.106 |
22.928 |
27.303 |
0 |
| Controle |
|
Rural |
|
|
|
55 |
23.691 |
0.958 |
24.418 |
1.103 |
24.129 |
0.702 |
22.735 |
25.522 |
56 |
23.607 |
0.944 |
24.821 |
1.156 |
24.573 |
0.775 |
23.037 |
26.110 |
-1 |
| Controle |
|
Urbana |
|
|
|
11 |
22.455 |
3.178 |
22.091 |
2.462 |
22.803 |
1.571 |
19.687 |
25.919 |
11 |
22.455 |
3.178 |
22.091 |
2.462 |
22.752 |
1.748 |
19.285 |
26.220 |
0 |
| Experimental |
|
Rural |
|
|
|
34 |
22.618 |
1.247 |
24.059 |
1.346 |
24.639 |
0.894 |
22.865 |
26.412 |
34 |
22.618 |
1.247 |
24.059 |
1.346 |
24.591 |
0.995 |
22.618 |
26.565 |
0 |
| Experimental |
|
Urbana |
|
|
|
5 |
26.200 |
3.338 |
23.400 |
3.187 |
21.078 |
2.336 |
16.443 |
25.712 |
5 |
26.200 |
3.338 |
23.400 |
3.187 |
21.105 |
2.600 |
15.947 |
26.264 |
0 |
| Controle |
|
|
Branca |
|
|
11 |
23.909 |
1.919 |
25.727 |
2.501 |
24.831 |
1.671 |
21.502 |
28.160 |
11 |
23.909 |
1.919 |
25.727 |
2.501 |
24.831 |
1.671 |
21.502 |
28.160 |
0 |
| Controle |
|
|
Parda |
|
|
45 |
23.156 |
1.120 |
23.622 |
1.178 |
23.301 |
0.825 |
21.656 |
24.945 |
45 |
23.156 |
1.120 |
23.622 |
1.178 |
23.301 |
0.825 |
21.656 |
24.945 |
0 |
| Experimental |
|
|
Branca |
|
|
5 |
21.200 |
2.478 |
28.000 |
3.332 |
29.171 |
2.477 |
24.235 |
34.106 |
5 |
21.200 |
2.478 |
28.000 |
3.332 |
29.171 |
2.477 |
24.235 |
34.106 |
0 |
| Experimental |
|
|
Parda |
|
|
18 |
21.389 |
2.104 |
22.111 |
1.937 |
23.138 |
1.309 |
20.530 |
25.745 |
18 |
21.389 |
2.104 |
22.111 |
1.937 |
23.138 |
1.309 |
20.530 |
25.745 |
0 |
| Controle |
|
|
|
6 ano |
|
26 |
19.423 |
1.089 |
18.692 |
0.979 |
21.510 |
1.236 |
19.066 |
23.953 |
26 |
19.423 |
1.089 |
18.692 |
0.979 |
21.510 |
1.236 |
19.066 |
23.953 |
0 |
| Controle |
|
|
|
7 ano |
|
28 |
23.286 |
1.129 |
26.464 |
1.549 |
26.753 |
1.152 |
24.474 |
29.031 |
28 |
23.286 |
1.129 |
26.464 |
1.549 |
26.753 |
1.152 |
24.474 |
29.031 |
0 |
| Controle |
|
|
|
8 ano |
|
17 |
23.882 |
2.592 |
24.000 |
2.329 |
23.898 |
1.478 |
20.975 |
26.820 |
17 |
23.882 |
2.592 |
24.000 |
2.329 |
23.898 |
1.478 |
20.975 |
26.820 |
0 |
| Controle |
|
|
|
9 ano |
|
27 |
29.148 |
1.253 |
28.852 |
1.475 |
25.301 |
1.238 |
22.854 |
27.749 |
27 |
29.148 |
1.253 |
28.852 |
1.475 |
25.301 |
1.238 |
22.854 |
27.749 |
0 |
| Experimental |
|
|
|
6 ano |
|
13 |
22.385 |
1.960 |
23.538 |
1.742 |
24.417 |
1.693 |
21.069 |
27.765 |
13 |
22.385 |
1.960 |
23.538 |
1.742 |
24.417 |
1.693 |
21.069 |
27.765 |
0 |
| Experimental |
|
|
|
7 ano |
|
13 |
23.692 |
2.129 |
23.077 |
2.330 |
23.099 |
1.690 |
19.757 |
26.441 |
13 |
23.692 |
2.129 |
23.077 |
2.330 |
23.099 |
1.690 |
19.757 |
26.441 |
0 |
| Experimental |
|
|
|
8 ano |
|
14 |
22.357 |
2.247 |
24.857 |
2.323 |
25.753 |
1.632 |
22.527 |
28.980 |
14 |
22.357 |
2.247 |
24.857 |
2.323 |
25.753 |
1.632 |
22.527 |
28.980 |
0 |
| Experimental |
|
|
|
9 ano |
|
8 |
25.250 |
1.221 |
26.750 |
2.448 |
25.752 |
2.157 |
21.486 |
30.018 |
8 |
25.250 |
1.221 |
26.750 |
2.448 |
25.752 |
2.157 |
21.486 |
30.018 |
0 |
| Controle |
|
|
|
|
1st quintile |
17 |
13.529 |
0.550 |
17.294 |
0.915 |
22.588 |
2.616 |
17.411 |
27.766 |
17 |
13.529 |
0.550 |
17.294 |
0.915 |
22.436 |
2.823 |
16.848 |
28.023 |
0 |
| Controle |
|
|
|
|
2nd quintile |
27 |
19.296 |
0.287 |
20.444 |
1.022 |
22.254 |
1.323 |
19.636 |
24.872 |
28 |
19.286 |
0.276 |
21.393 |
1.367 |
23.146 |
1.411 |
20.354 |
25.939 |
-1 |
| Controle |
|
|
|
|
3rd quintile |
31 |
26.097 |
0.389 |
25.613 |
1.079 |
23.312 |
1.399 |
20.543 |
26.082 |
31 |
26.097 |
0.389 |
25.613 |
1.079 |
23.358 |
1.518 |
20.353 |
26.363 |
0 |
| Controle |
|
|
|
|
4th quintile |
9 |
31.667 |
0.471 |
32.556 |
2.416 |
26.889 |
3.035 |
20.880 |
32.898 |
9 |
31.667 |
0.471 |
32.556 |
2.416 |
27.022 |
3.288 |
20.514 |
33.531 |
0 |
| Experimental |
|
|
|
|
1st quintile |
9 |
12.111 |
0.964 |
17.333 |
2.279 |
23.485 |
3.199 |
17.152 |
29.817 |
9 |
12.111 |
0.964 |
17.333 |
2.279 |
23.310 |
3.454 |
16.472 |
30.147 |
0 |
| Experimental |
|
|
|
|
2nd quintile |
8 |
19.125 |
0.639 |
19.000 |
2.188 |
20.913 |
2.142 |
16.672 |
25.153 |
8 |
19.125 |
0.639 |
19.000 |
2.188 |
20.848 |
2.314 |
16.268 |
25.429 |
0 |
| Experimental |
|
|
|
|
3rd quintile |
21 |
25.286 |
0.432 |
27.238 |
1.322 |
25.428 |
1.443 |
22.571 |
28.285 |
21 |
25.286 |
0.432 |
27.238 |
1.322 |
25.460 |
1.565 |
22.363 |
28.557 |
0 |
| Experimental |
|
|
|
|
4th quintile |
9 |
31.333 |
0.527 |
27.667 |
1.581 |
22.202 |
2.969 |
16.324 |
28.079 |
9 |
31.333 |
0.527 |
27.667 |
1.581 |
22.330 |
3.216 |
15.963 |
28.696 |
0 |